For teams of 1 to 30
Custom AI that does the work your team does by hand.
Move faster without hiring.
Automations, dashboards, internal tools, and AI workers, built into the systems you already run.
See what AI could do for your team. Try the worker on the right, free in 60 seconds.Or get a 30-min scope call↗
Readiness checkAI worker · live
Tell me who you are and I will show you the first AI worker we would build for your team.
Pick one to start
/ what we build
From a single automation to your whole operation.
Most teams start with one painful workflow. We build whatever closes the gap, from a small automation to a full system that runs across the tools you already use.
Workflow automations
Repetitive, multi-step work that runs itself.
ExampleWeekly client reports, drafted from your CRM, project tool, and analytics, ready Monday morning.
Custom dashboards
One screen with the numbers that matter, pulled live from every tool.
ExampleClient health in real time, combining CRM activity, campaign performance, and open tasks.
Internal tools
Small custom apps that make a job far faster for your team.
ExampleA drafter that turns a sales-call recording into a proposal that is most of the way written, in minutes.
Customer-facing tools and pages
AI-powered things your customers actually touch.
ExampleA smart intake form, an instant quoting page, or a support assistant that speaks in your voice.
AI workers
An AI that owns a role and works alongside your people.
ExampleAn account manager that drafts updates and flags issues, or a researcher that writes sourced briefings.
A full AI operating system
Everything above, wired into one system that runs your operation.
ExampleSeveral workers, automations, and dashboards orchestrated across your entire stack.
/ how it works in real life
Real workflows, before and after.
Two examples of the same boring work handed to AI. You stay in control. The AI does the grind.
The Monday status reports
Before
Sunday, 23:00. The founder writes ten client updates by hand, half an hour each, getting worse as the night goes on.
AI steps in
AI pulls the week's activity from your CRM, project tool, and analytics, and drafts every report in your voice on Sunday night.
After
Monday, 06:00. Ten drafts are waiting. You skim each one, fix a line, and send. The night back is yours.
Ten client reports, drafted overnight
A workflow automation, your brand voice, and a memory for every client.
From sales call to proposal
Before
After every discovery call, someone spends two to four hours turning notes into a proposal. Quality slips when they are tired.
AI steps in
AI reads the call recording, maps what the prospect asked for to your scope and pricing, and drafts the proposal.
After
An hour later a draft is sitting in your docs. You polish the last stretch and send it the same day.
A proposal, scoped and priced
An internal tool, triggered by your call recordings.
/ how it all fits together
Five layers. Each one compounds.
Everything we build, from a single automation to a full operating system, sits on the same five-layer system. Every engagement delivers all five; what scales is scope. First Worker covers one workflow, Department Build covers a function, Operating Partner rolls across the company.
/ FOUNDATION
Universal Knowledge Layer
Everything your business knows, made accessible to AI. Docs, conversations, decisions, customer data. The foundation every other layer reads from and writes to.
/ MEANING
Context Synthesis
Raw data turned into understanding. The system knows the relationships between everything you know: people, accounts, deals, threads, history. Context retrieval, not search.
/ EXECUTION
Autonomous Action
Agents act across your tools. Deterministic where it matters (rules, compliance), judgment where it doesn't (drafts, routing, prioritization). Every action logged.
/ JUDGMENT
Human Collaboration
Your team directs the system. The system prepares decision packages with full context; your people approve, edit, or override. The judgment work stays with humans.
/ COMPOUNDING
Continuous Evolution
Outcomes feed back into the system. Loops sharpen. Your moat compounds month over month, because the system learns from every decision your team makes.
/ built on your stack
Your tools. Working as one.
Google Workspace, Slack, Notion, Linear, HubSpot, Stripe, Instantly, your CRM, whatever your stack is. The architecture stays the same; only the connections change. Below: a glimpse of what the system is doing right now across one team's stack.
#
Slack · #supportlive
AI · drafting reply
AI handled 14 threads · 2 escalated
live
Q3 Strategy Review
AI · captured from call
Knowledge captured 27 entries this week
live
NAT-204
NAT-203
NAT-202
AI · priority assigned
AI triaged 9 issues · 1 to founder
live
Gmail · inbox
AI · drafted reply
AI drafted 23 replies · all approved
live
CRM · contacts
hot
AI · enriched + scored
AI enriched 116 contacts · 4 marked hot
live
Payments · today
flagged
AI · reconciled + matched
AI reconciled 312 payments · 3 flagged
These are examples. The same architecture connects to whatever your team actually runs on, from Stripe and n8n to your accounting suite or your industry CRM.
/ the work
Three ways to work together.
Every engagement delivers all five layers. What scales between tiers is scope, time, and how deep we go. Compare line-by-line, then pick what fits.
First Worker One workflow 2 weeks | MOST CHOSEN Department Build One department 6 to 10 weeks | Operating Partner Expanding across Ongoing | |
|---|---|---|---|
| Workers + integrations | |||
| AI workers shipped to production | 1 worker | 3 to 5 workers | 3 to 5 + new each quarter |
| Lives inside your existing stack | The workflow's tools | Whole department's stack | Cross-department |
| Workers orchestrate with each other | — | Cross-department | |
| Custom-fitted to your data + voice | |||
| Knowledge + handoff | |||
| Searchable knowledge of every decision | The workflow | Department-wide | Company-wide (growing) |
| Approve / edit / decline interface for your team | |||
| Team training + handoff doc | Handoff doc | Full team trained | Continuous |
| You own the architecture (no lock-in) | |||
| Ongoing | |||
| New workers shipped each quarter | — | — | 2 to 4 per quarter |
| Architecture upgraded as models advance | — | — | |
| Monthly impact + next-quarter review | — | — | |
| Continuous Evolution layer active | Captures outcomes | Tuned monthly | Tuned weekly |
| Starting investment | from 2.500 EUR one-time | from 8.000 EUR one-time | from 2.000 EUR per month |
| Get a 30-min scope call | Get a 30-min scope call | Get a 30-min scope call | |
Not sure which fits? Most teams start with First Worker, then move to Department Build once the first worker proves its weight. Operating Partner unlocks once a department is fully running.
/ the proof
Outcomes, not promises.
Anthropic and OpenAI just put 5.5 billion dollars into doing exactly this for the Fortune 500. We do it for teams of 1 to 30. Every claim below is cited or marked as a deliverable.
01
2weeks
First AI worker in production
First Worker deliverable. One custom worker, on your stack, in your voice, doing real work by week two.
// NativeAI deliverable
02
30 to 50%
Routine work automated
Typical first-quarter range for an AI-native rollout: the share of repeatable, rules-based work that stops needing a human at all.
// Framework PDF §4.4 · industry benchmark
03
66%
Klarna's customer service handled by AI
Resolution time dropped from 11 minutes to 2 minutes. Equivalent to 700 full-time agents. Same architecture, custom-fitted.
// Public benchmark · Framework §5.1
04
Forever
Knowledge that compounds
What your team learns this month makes the system sharper next month. Nothing leaves with people when they leave.
// NativeAI deliverable
/ why us
Why companies choose to build
with us.
01
Process first, tech second
95% of AI projects fail because they start with tools, not workflow. We start with an audit and design the architecture before any agent gets built.
02
Custom, not platform
No vendor lock-in. We build into the tools you already use, not a separate platform you have to learn. The architecture is yours.
03
Ship in weeks, not quarters
First worker live in two weeks. No endless pilot phases. Real production deliverables before you sign the next phase.
04
Operator-built
Designed by someone who runs AI-native operations every day, not by a consultant explaining a slide deck. The framework is shipped substance, not theory.
/ the alternatives
The difference is clear.
The honest comparison against the three things teams actually weigh us against.
| NativeAI | DIY automation | Generic AI tool | Hiring in-house | |
|---|---|---|---|---|
| Time to first real output | A working AI worker in about two weeks | Weeks of wiring, then it breaks | Live in minutes, shallow forever | Months to hire, longer to ramp |
| Handles judgment, not just scripts | Loops that escalate exceptions and decide | Fixed rules, fails on the edge cases | Generic answers, no business context | Yes, but only one person's worth |
| Improves over time | Compounds on what your team teaches it | Static until you rebuild it | Improves for everyone but you | Walks out the door when they leave |
| Runs inside your tools | Native in the stack you already use | Brittle integrations you maintain | Another tab, another silo | Depends who you hire |
| What it costs to scale | Scope grows, no per-seat tax | More glue, more maintenance | Per-seat pricing punishes growth | Linear headcount cost |
| Who owns it | You do, fully, including the playbook | You, plus the upkeep burden | The vendor does | You, until they leave |
/ questions
Before you book.
/ start here
If you would rebuild your company around AI today,
this is the conversation worth having.
A short discovery call. We map one workflow worth building first, scope the right starting point, and you walk away with a working brief either way.
30 min · async-first · no obligation